
# load libraries
import geopandas as gpd # vector data
import pandas as pd # tabular data, loading CSVs
import numpy as np # numeric data
from util import *
import matplotlib.pyplot as plt # plotting
from tqdm.auto import tqdm # progress bars
import json
from shapely.geometry import Point, shape # creating points
import requests # web requests
from pprint import pprint
plt.rcParams['figure.figsize'] = (20, 20)


ls()

Total: 444.0MB


df = gpd.read_file("input/Modified_Community_Boards_SHP.zip")
df.OBJECTID = df.OBJECTID.astype(int)
df = df.set_index("OBJECTID", drop=True)
df = df.sort_index()
df


df = df.rename(columns={"Local_Area": "Name"})


df["Centroid_lon"] = df.centroid.x
df["Centroid_lat"] = df.centroid.y
df["Area"] = df.area


%%time
zones = gpd.read_file("input/MASTER_UP_BaseZone_SHP.zip")
zones

CPU times: user 16.1 s, sys: 482 ms, total: 16.5 s
Wall time: 16.5 s


zones[pd.isna(zones.ZONE_resol)]


zones.ZONE_resol = zones.ZONE_resol.fillna("Special")


zones.GROUPZONE_.value_counts()

General                 52461
Coastal                 42200
Residential             22398
Public Open Space        6667
Business                 3237
Rural                    2750
Special purpose zone      299
New growth                288
Name: GROUPZONE_, dtype: int64


zones.ZONE_resol.value_counts()

Road                                                         47012
Coastal - General Coastal Marine Zone                        26326
Coastal - Coastal Transition Zone                            15703
Residential - Mixed Housing Suburban Zone                     9775
Residential - Mixed Housing Urban Zone                        5864
Strategic Transport Corridor Zone                             4215
Residential - Single House Zone                               3766
Open Space - Conservation Zone                                3063
Open Space - Informal Recreation Zone                         2926
Residential - Terrace Housing and Apartment Building Zone     2134
Rural - Rural Production Zone                                 1025
Water                                                         1013
Business - Mixed Use Zone                                      782
Rural - Rural Coastal Zone                                     768
Business - Light Industry Zone                                 593
Business - Neighbourhood Centre Zone                           564
Residential - Rural and Coastal Settlement Zone                480
Open Space - Sport and Active Recreation Zone                  477
Business - Town Centre Zone                                    432
Residential - Large Lot Zone                                   379
Rural - Mixed Rural Zone                                       344
Rural - Countryside Living Zone                                326
Future Urban Zone                                              282
Business - Local Centre Zone                                   266
Hauraki Gulf Islands                                           221
Business - City Centre Zone                                    191
Open Space - Community Zone                                    183
Rural - Waitakere Ranges Zone                                  172
Business - Metropolitan Centre Zone                            160
Business - Heavy Industry Zone                                 130
Special Purpose - School Zone                                  126
Coastal - Mooring Zone                                         124
Business - General Business Zone                               103
Rural - Rural Conservation Zone                                 71
Rural - Waitakere Foothills Zone                                44
Special Purpose - Cemetery Zone                                 41
Special Purpose - Quarry Zone                                   34
Special Purpose - Maori Purpose Zone                            32
Special Purpose - Major Recreation Facility Zone                28
Special Purpose - Healthcare Facility and Hospital Zone         23
Coastal - Ferry Terminal Zone                                   20
Coastal - Marina Zone                                           20
Open Space - Civic Spaces Zone                                  18
Business - Business Park Zone                                   16
Special Purpose - Airports and Airfields Zone                    9
Green Infrastructure Corridor                                    6
Special Purpose - Tertiary Education Zone                        4
Coastal - Minor Port Zone                                        4
Coastal - Defence Zone                                           3
Special                                                          2
Name: ZONE_resol, dtype: int64


# Create a dictionary mapping the first n character of the zone description to the desired variable name
zones_of_interest = {
    "Residential": "Residential_area",
    "Residential - Single House Zone": "SH_area",
    "Residential - Mixed Housing Suburban Zone": "MHS_area",
    "Residential - Mixed Housing Urban Zone": "MHU_area",
    "Residential - Terrace Housing and Apartment Building Zone": "THA_area",
    "Residential - Large Lot Zone": "LL_area",
    "Future Urban Zone": "FU_area",
    "Hauraki Gulf Islands":  "HGI_area",
    "Business": "Business_area",
    "Rural": "Rural_area",
    "Open Space": "Open_area"
}


%%time
for k,v in tqdm(zones_of_interest.items()):
    subset = zones[zones.ZONE_resol.str.startswith(k)] # Just the matched zones (residential, business, rural etc)
    print(k, len(subset))
    clipped = gpd.clip(df, subset) # Clip the local areas to the matched zones
    df[v] = clipped.area # Store the resulting area. Unit is m²

Residential 22398
Residential - Single House Zone 3766
Residential - Mixed Housing Suburban Zone 9775
Residential - Mixed Housing Urban Zone 5864
Residential - Terrace Housing and Apartment Building Zone 2134
Residential - Large Lot Zone 379
Future Urban Zone 282
Hauraki Gulf Islands 221
Business 3237
Rural 2750
Open Space 6667
CPU times: user 6min 5s, sys: 415 ms, total: 6min 6s
Wall time: 6min 5s


skytower = Point(1757109.809, 5920500.841) # in NZGD2000 projection


df["Hdist_skytower"] = df.centroid.distance(skytower) # Unit is meters


df.plot(column="Hdist_skytower", legend=True)

<matplotlib.axes._subplots.AxesSubplot at 0x7fded247ce80>


coastline = df.dissolve().boundary.iloc[0]


df["Coast_indicator"] = df.intersects(coastline)
df[["Name", "Coast_indicator"]]


households = pd.read_csv("input/2013-mb-dataset-Total-New-Zealand-Household.csv")
households

/usr/local/lib/python3.8/dist-packages/IPython/core/interactiveshell.py:3165: DtypeWarning: Columns (1,2) have mixed types.Specify dtype option on import or set low_memory=False.
  has_raised = await self.run_ast_nodes(code_ast.body, cell_name,


key = "2013_Census_total_household_income_(grouped)(2)(3)(4)_for_households_in_occupied_private_dwellings_Median_household_income_($)(18)(23)"
households_with_income = households[households.Area_Code_and_Description.str.startswith("MB") & (households[key] != "..C") & (households[key] != "*")].copy()
print(len(households_with_income))
households_with_income[key] = households_with_income[key].astype(int)
households_with_income[key].plot(kind="hist", bins=20, figsize=(10,10))

40300

<matplotlib.axes._subplots.AxesSubplot at 0x7fa51f7c7940>


meshblocks = gpd.read_file("input/statsnzpopulation-by-meshblock-2013-census-FGDB.zip!population-by-meshblock-2013-census.gdb")
meshblocks


meshblocks.geometry = meshblocks.representative_point()


meshblocks = meshblocks.merge(households, left_on="Meshblock", right_on="Area_Code_and_Description")


meshblocks = gpd.sjoin(df, meshblocks, op="intersects")
meshblocks


group = meshblocks.groupby("OBJECTID")


df["Census2013_population"] = group["Population_Count_Usual_Resident_2013"].sum()


df["Census2013_dwellings"] = group["2013_Census_total_households_in_occupied_private_dwellings"].sum()


meshblocks_with_income = meshblocks[(meshblocks[key] != "..C") & (meshblocks[key] != "*")].copy()
meshblocks_with_income[key] = meshblocks_with_income[key].astype(int)
meshblocks_with_income


meshblocks_with_income["income*dwellings"] = meshblocks_with_income[key] * meshblocks_with_income["2013_Census_total_households_in_occupied_private_dwellings"]
meshblocks_with_income["income*dwellings"]

OBJECTID
1      405000.0
1     2274300.0
1      331200.0
1     1500000.0
1      624000.0
        ...    
33    2565000.0
33    3622500.0
33    4860000.0
33    3900000.0
33    3331800.0
Name: income*dwellings, Length: 10830, dtype: float64


group = meshblocks_with_income.groupby("OBJECTID")
df["Census2013_avg_HH_income"] = group["income*dwellings"].sum() / group["2013_Census_total_households_in_occupied_private_dwellings"].sum()


df[["Name", "Census2013_population", "Census2013_dwellings", "Census2013_avg_HH_income"]].sort_values(by="Census2013_population", ascending=False)

df


df.to_csv("output/Local_Area.csv")


mb = gpd.read_file("input/statsnzmeshblock-higher-geographies-2018-generalised-FGDB.zip!meshblock-higher-geographies-2018-generalised.gdb")


mb = mb[(mb.TA2018_V1_00_NAME == "Auckland") & mb.LANDWATER_NAME.isin(["Mainland", "Island"])]


mb = mb.rename(columns={"MB2018_V1_00": "Code"})
mb["Centroid_lon"] = mb.centroid.x
mb["Centroid_lat"] = mb.centroid.y
mb


mb = mb.set_index("Code")


skytower = Point(1757109.809, 5920500.841)
mb["Hdist_skytower"] = mb.centroid.distance(skytower) # meters
mb


mb[mb.Hdist_skytower < 5000].plot(column="Hdist_skytower", legend=True)
plt.title("Meshblocks <5KM from the Skytower")

Text(0.5, 1.0, 'Meshblocks <5KM from the Skytower')


coastline = gpd.read_file("input/lds-nz-coastlines-and-islands-polygons-topo-150k-FGDB.zip!nz-coastlines-and-islands-polygons-topo-150k.gdb")


coastline.boundary.plot()

<AxesSubplot:>


coastline = coastline.dissolve().boundary.iloc[0]


%%time
mb["Hdist_coast"] = [point.distance(coastline) for point in tqdm(mb.centroid)]

CPU times: user 1min 22s, sys: 160 ms, total: 1min 22s
Wall time: 1min 22s


mb["Hdist_coast"].describe() # meters

count    13441.000000
mean      1596.010824
std       1792.477098
min          0.488582
25%        444.163564
50%       1076.033671
75%       2116.959738
max      16027.664069
Name: Hdist_coast, dtype: float64


mb.plot(column="Hdist_coast", legend=True)

<AxesSubplot:>


len(mb)

13441


BASE_URL = "http://osrm.auckland-cer.cloud.edu.au"
def drive(points, to):
    points = [f"{point.x},{point.y}" for point in points]
    points = ";".join(points)
    result = requests.get(f"{BASE_URL}/table/v1/driving/{to};{points}?destinations=0&annotations=duration,distance")
    return result.json()


skytower = "174.76218883819053,-36.848429166610735"


%%time
result = drive(points=mb.centroid.to_crs(epsg=4326), to=skytower)

CPU times: user 1.24 s, sys: 57 µs, total: 1.24 s
Wall time: 1min 15s


result.keys()

dict_keys(['code', 'sources', 'destinations', 'durations', 'distances'])


mb["Mdist_skytower"] = [r[0] for r in result["distances"][1:]]
mb["Mdist_skytower"].describe() # Units are meters

count     13441.000000
mean      21083.293989
std       15682.240482
min          59.500000
25%       11506.700000
50%       17201.300000
75%       24218.100000
max      136633.200000
Name: Mdist_skytower, dtype: float64


mb.plot(column="Mdist_skytower", legend=True)

<AxesSubplot:>


mb[mb.Mdist_skytower < 5000].plot(column="Mdist_skytower", legend=True)
plt.title("Meshblocks <5KM driving from the Skytower")

Text(0.5, 1.0, 'Meshblocks <5KM driving from the Skytower')


# Some strange gaps there - let's investigate
points = mb[(mb.Hdist_skytower < 1000) & (mb.Mdist_skytower > 5000)]
points = [f"{point.x},{point.y}" for point in points.centroid.to_crs(epsg=4326)]
points

['174.75624867471396,-36.85383185528419',
 '174.75345762242358,-36.850503483712096',
 '174.75551843174853,-36.8526249602678',
 '174.75734772557055,-36.855642008978414']


routes = []
for point in points:
    route_result = requests.get(f"{BASE_URL}/route/v1/driving/{skytower};{point}?geometries=geojson")
    routes.extend(route_result.json()["routes"])
routes

[{'geometry': {'coordinates': [[174.762021, -36.848507],
    [174.762081, -36.848111],
    [174.761663, -36.847994],
    [174.760279, -36.851092],
    [174.756401, -36.856918],
    [174.756298, -36.858244],
    [174.757545, -36.859416],
    [174.761092, -36.859842],
    [174.763064, -36.860822],
    [174.764566, -36.862759],
    [174.766927, -36.864565],
    [174.768415, -36.867436],
    [174.774277, -36.87211],
    [174.774115, -36.872578],
    [174.773385, -36.872143],
    [174.771509, -36.870028],
    [174.768209, -36.867454],
    [174.766746, -36.864588],
    [174.764693, -36.863068],
    [174.76298, -36.861326],
    [174.761243, -36.860494],
    [174.758161, -36.860144],
    [174.756868, -36.859449],
    [174.755729, -36.85724],
    [174.756435, -36.854967],
    [174.756232, -36.853838]],
   'type': 'LineString'},
  'legs': [{'steps': [],
    'distance': 6623.4,
    'duration': 471.2,
    'summary': '',
    'weight': 482.5}],
  'distance': 6623.4,
  'duration': 471.2,
  'weight_name': 'routability',
  'weight': 482.5},
 {'geometry': {'coordinates': [[174.762021, -36.848507],
    [174.761973, -36.848406],
    [174.762081, -36.848111],
    [174.761663, -36.847994],
    [174.760403, -36.850878],
    [174.75828, -36.854101],
    [174.758023, -36.853973],
    [174.756934, -36.853677],
    [174.756692, -36.853485],
    [174.756478, -36.853123],
    [174.756232, -36.85299],
    [174.755149, -36.853046],
    [174.75503, -36.853003],
    [174.754968, -36.852487],
    [174.754131, -36.851451],
    [174.753566, -36.85047]],
   'type': 'LineString'},
  'legs': [{'steps': [],
    'distance': 1487.9,
    'duration': 201.1,
    'summary': '',
    'weight': 212.4}],
  'distance': 1487.9,
  'duration': 201.1,
  'weight_name': 'routability',
  'weight': 212.4},
 {'geometry': {'coordinates': [[174.762021, -36.848507],
    [174.762081, -36.848111],
    [174.760362, -36.847639],
    [174.756384, -36.84814],
    [174.75646, -36.846119],
    [174.751793, -36.844936],
    [174.749101, -36.843285],
    [174.746338, -36.842436],
    [174.744751, -36.840654],
    [174.742077, -36.838893],
    [174.741435, -36.837888],
    [174.74165, -36.836424],
    [174.749967, -36.823771],
    [174.750195, -36.822768],
    [174.749701, -36.81919],
    [174.751533, -36.813923],
    [174.750898, -36.812294],
    [174.753046, -36.811822],
    [174.753968, -36.812071],
    [174.754297, -36.812877],
    [174.753791, -36.813549],
    [174.75206, -36.814361],
    [174.751215, -36.815353],
    [174.750131, -36.819403],
    [174.750332, -36.822928],
    [174.750102, -36.823818],
    [174.741981, -36.836162],
    [174.74164, -36.837484],
    [174.742147, -36.838632],
    [174.744931, -36.840513],
    [174.746304, -36.842152],
    [174.749683, -36.843296],
    [174.752518, -36.845063],
    [174.752984, -36.845821],
    [174.753572, -36.850065],
    [174.755481, -36.852645]],
   'type': 'LineString'},
  'legs': [{'steps': [],
    'distance': 11247.2,
    'duration': 783.1,
    'summary': '',
    'weight': 794.4}],
  'distance': 11247.2,
  'duration': 783.1,
  'weight_name': 'routability',
  'weight': 794.4},
 {'geometry': {'coordinates': [[174.762021, -36.848507],
    [174.761973, -36.848406],
    [174.762081, -36.848111],
    [174.761663, -36.847994],
    [174.760403, -36.850878],
    [174.758622, -36.85365],
    [174.757737, -36.854764],
    [174.757261, -36.855607]],
   'type': 'LineString'},
  'legs': [{'steps': [],
    'distance': 1022.2,
    'duration': 135,
    'summary': '',
    'weight': 146.3}],
  'distance': 1022.2,
  'duration': 135,
  'weight_name': 'routability',
  'weight': 146.3}]


route_df = pd.read_json(json.dumps(routes))
route_df["geometry"] = route_df['geometry'].apply(shape)
route_df = gpd.GeoDataFrame(route_df)
route_df


from shapely.geometry import shape
ax = mb[mb.Mdist_skytower < 5000].to_crs(epsg=4326).plot(column="Mdist_skytower", legend=True)
route_df[route_df["distance"]>10000].plot(column="distance", legend=True, ax=ax, linewidth=3, cmap="plasma")

<AxesSubplot:>


points = mb[(mb.Hdist_skytower < 1000) & (mb.Mdist_skytower > 5000)]
points = [f"{point.y},{point.x}" for point in points.centroid.to_crs(epsg=4326)]
for point in points:
    print(f"https://map.project-osrm.org/?loc={point}&loc=-36.848429166610735,174.76218883819053&srv=0")

https://map.project-osrm.org/?loc=-36.85383185528419,174.75624867471396&loc=-36.848429166610735,174.76218883819053&srv=0
https://map.project-osrm.org/?loc=-36.850503483712096,174.75345762242358&loc=-36.848429166610735,174.76218883819053&srv=0
https://map.project-osrm.org/?loc=-36.8526249602678,174.75551843174853&loc=-36.848429166610735,174.76218883819053&srv=0
https://map.project-osrm.org/?loc=-36.855642008978414,174.75734772557055&loc=-36.848429166610735,174.76218883819053&srv=0


mb["Mtime_skytower"] = [r[0] for r in result["durations"][1:]]
mb["Mtime_skytower"].describe() # Units are seconds

count    13441.000000
mean      1594.367316
std       3358.619612
min         15.100000
25%        867.000000
50%       1163.400000
75%       1514.500000
max      70521.800000
Name: Mtime_skytower, dtype: float64


mb[mb.Mtime_skytower < 60*20].plot(column="Mtime_skytower", legend=True)
plt.title("Meshblocks < 20 minutes drive from the Skytower")

Text(0.5, 1.0, 'Meshblocks < 20 minutes drive from the Skytower')


%%time
neg = drive(points=mb.centroid.translate(xoff=-100).to_crs(epsg=4326), to=skytower)

CPU times: user 1.84 s, sys: 0 ns, total: 1.84 s
Wall time: 1min 11s


%%time
pos = drive(points=mb.centroid.translate(xoff=100).to_crs(epsg=4326), to=skytower)

CPU times: user 2.18 s, sys: 0 ns, total: 2.18 s
Wall time: 1min 18s


mb["Mdist_skytower_center"] = mb["Mdist_skytower"]
mb["Mdist_skytower_west"] = [r[0] for r in neg["distances"][1:]]
mb["Mdist_skytower_east"] = [r[0] for r in pos["distances"][1:]]
mb[["Mdist_skytower_center", "Mdist_skytower_west", "Mdist_skytower_east"]].describe()


mb["Mdist_skytower"] = mb[["Mdist_skytower", "Mdist_skytower_west", "Mdist_skytower_east"]].min(axis=1)
(mb["Mdist_skytower_center"] - mb["Mdist_skytower"]).describe()

count    13441.000000
mean       200.675322
std        464.749034
min          0.000000
25%         26.300000
50%         92.600000
75%        193.500000
max      12976.900000
dtype: float64


mb[mb.Mdist_skytower < 5000].plot(column="Mdist_skytower", legend=True)
plt.title("Meshblocks <5KM driving from the Skytower")

Text(0.5, 1.0, 'Meshblocks <5KM driving from the Skytower')


# Still not quite right - try shift meshblock centroid to nearest parcel centroid
nz_parcels = gpd.read_file('input/lds-nz-primary-parcels-FGDB.zip!nz-primary-parcels.gdb')
print(nz_parcels.crs)

epsg:4167


nz_parcels = nz_parcels.to_crs(mb.crs)


nz_parcels.parcel_intent.value_counts()

DCDB                                                                       342691
Fee Simple Title                                                           138388
Road                                                                        50033
Hydro                                                                        1909
Legalisation                                                                 1630
Vesting on Deposit for Local Purpose Reserve                                 1327
Vesting on Deposit for Recreation Reserve (Territorial Authority)             526
Railway                                                                       249
Common Marine and Coastal Area (Sec 237A(1)(b) RM Act)                        173
Maori                                                                         106
Residue Parcel                                                                 74
Vesting on Deposit in the Crown (Sec 237A(1)(b) RM Act)                        39
Vesting on Deposit in the Territorial Authority (Sec 237A(1)(a) RM Act)        38
Vesting on Deposit for Recreation Reserve (Crown)                              33
Reclamation Area                                                                9
Statutory                                                                       9
Vesting on Deposit for Historic Reserve (Territorial Authority)                 8
Vesting on Deposit for Scenic Reserve (Territorial Authority)                   8
Vesting on Deposit in Lieu of a Reserve (Territorial Authority)                 8
Strata                                                                          7
Lease                                                                           7
Streambed                                                                       5
Vesting on Deposit for Scenic Reserve (Crown)                                   4
Vesting on Deposit in the Crown (Sec 239(1)(c) RM Act)                          4
Accretion                                                                       2
Vesting on Deposit for Nature Reserve (Crown)                                   1
UNKN                                                                            1
Vesting on Deposit in Lieu of a Reserve (Crown)                                 1
Name: parcel_intent, dtype: int64


len(nz_parcels)

537290


nz_parcels = nz_parcels[~nz_parcels.parcel_intent.isin(["Hydro", "Road", "Railway"])]
len(nz_parcels)

485099


from scipy.spatial import cKDTree
btree = cKDTree(np.array(list(nz_parcels.centroid.apply(lambda x: (x.x, x.y)))))


%%time
dist, idx = btree.query(np.array(list(mb.centroid.apply(lambda x: (x.x, x.y)))), k=1)
pd.Series(dist).describe()

CPU times: user 558 ms, sys: 54 µs, total: 558 ms
Wall time: 557 ms

count    13441.000000
mean        28.794665
std         58.531502
min          0.001892
25%          9.260330
50%         15.164456
75%         23.871677
max       1672.178656
dtype: float64


%%time
shifted_to_nearest_parcel_centroid = drive(points=nz_parcels.centroid.iloc[idx].to_crs(epsg=4326), to=skytower)

CPU times: user 2.6 s, sys: 9.63 ms, total: 2.61 s
Wall time: 1min 13s


mb[["Mdist_skytower", "Mdist_skytower_west", "Mdist_skytower_east", "Mdist_skytower_nearest_parcel_centroid"]].describe()


mb["Mdist_skytower_nearest_parcel_centroid"] = [r[0] for r in shifted_to_nearest_parcel_centroid["distances"][1:]]


ax = mb[mb.Hdist_skytower < 4000].to_crs(epsg=4326).plot(column="Mdist_skytower", legend=True)
mb[mb.Hdist_skytower < 4000].centroid.to_crs(epsg=4326).plot(ax=ax, color="red")
nz_parcels.centroid.iloc[idx[mb.Hdist_skytower < 4000]].to_crs(epsg=4326).plot(ax=ax, color="blue")

<AxesSubplot:>


mb["Mdist_skytower"] = mb[["Mdist_skytower", "Mdist_skytower_west", "Mdist_skytower_east", "Mdist_skytower_nearest_parcel_centroid"]].min(axis=1)
(mb["Mdist_skytower_center"] - mb["Mdist_skytower"]).describe()

count    13441.000000
mean       209.142385
std        480.388408
min          0.000000
25%         31.000000
50%         94.600000
75%        199.000000
max      12976.900000
dtype: float64


mb[mb.Mdist_skytower < 5000].plot(column="Mdist_skytower", legend=True)
plt.title("Meshblocks <5KM driving from the Skytower")

Text(0.5, 1.0, 'Meshblocks <5KM driving from the Skytower')


mb.drop(columns="geometry").to_csv("output/2018_Meshblocks.csv")

	name	filesize (MB)	last modified
0	2013-mb-dataset-Total-New-Zealand-Household.csv	37.12	2021-07-20 09:51:53.970000
1	CSTDAT8700_Output_20210607.zip	217.69	2021-06-30 09:37:35.480000
2	MASTER_UP_BaseZone_SHP.zip	66.92	2021-07-20 09:51:54.490000
3	Modified_Community_Boards_SHP.zip	1.30	2021-06-30 11:55:11.883103
4	lds-nz-coastlines-and-islands-polygons-topo-150k-FGDB.zip	4.25	2021-08-06 11:54:33.440000
5	statsnzmeshblock-higher-geographies-2018-generalised-FGDB.zip	34.54	2021-08-06 11:54:34.670000
6	statsnzpopulation-by-meshblock-2013-census-FGDB.zip	82.11	2021-07-20 09:51:54.950000

	Local_Area	geometry
OBJECTID
1	Auckland Central	POLYGON ((1755802.315 5921956.091, 1755861.443...
2	Beach Haven-Birkenhead-Northcote	POLYGON ((1757287.966 5925962.738, 1757211.333...
3	Botany	POLYGON ((1770748.846 5912611.168, 1770889.893...
4	Devonport-Takapuna	POLYGON ((1755276.581 5932026.336, 1755278.305...
5	East Coast Bays	POLYGON ((1756125.006 5940268.048, 1756139.852...
6	Franklin-Beachlands-Hunua	MULTIPOLYGON (((1804302.354 5890738.079, 17905...
7	Franklin-Pukekohe	POLYGON ((1765085.620 5897344.807, 1765096.229...
8	Franklin-Waiuku	POLYGON ((1744829.308 5899882.633, 1744835.760...
9	Henderson-Massey	POLYGON ((1745963.138 5923457.510, 1745945.884...
10	Hibiscus Coast	MULTIPOLYGON (((1752023.352 5954803.281, 17520...
11	Howick	POLYGON ((1771447.460 5916900.636, 1771474.703...
12	Mangere-Otahuhu	MULTIPOLYGON (((1766142.746 5910838.845, 17661...
13	Manurewa	POLYGON ((1769432.488 5904664.673, 1769471.312...
14	Maungakiekie	POLYGON ((1759418.674 5915517.660, 1759596.351...
15	Mt Albert	POLYGON ((1756291.061 5918265.104, 1756266.530...
16	Mt Eden	POLYGON ((1751910.995 5920299.659, 1751917.632...
17	Orakei	POLYGON ((1759833.395 5920429.502, 1759837.143...
18	Otara	MULTIPOLYGON (((1765765.640 5909501.655, 17657...
19	Pakuranga	POLYGON ((1769523.742 5920466.582, 1769527.337...
20	Papakura	POLYGON ((1772188.699 5902202.011, 1772192.566...
21	Papatoetoe	POLYGON ((1765105.421 5908611.893, 1765113.661...
22	Puketapapa	POLYGON ((1753132.892 5915040.177, 1753141.101...
23	Rodney-Dairy Flat	POLYGON ((1746055.938 5948651.765, 1746067.112...
24	Rodney-Helensville	MULTIPOLYGON (((1710567.626 5967865.416, 17106...
25	Rodney-Kumeu-Riverhead	POLYGON ((1742532.808 5931237.574, 1742490.377...
26	Rodney-Warkworth	MULTIPOLYGON (((1761692.564 5984690.018, 17617...
27	Rodney-Wellsford	MULTIPOLYGON (((1746318.204 6000215.757, 17464...
28	Tamaki	POLYGON ((1765594.677 5917986.938, 1765609.808...
29	Titirangi	POLYGON ((1749038.038 5910572.842, 1749034.230...
30	Upper Harbour Local Board Area	MULTIPOLYGON (((1743880.554 5928979.708, 17438...
31	Waiheke	MULTIPOLYGON (((1793981.864 5931917.843, 17940...
32	Waitakere	MULTIPOLYGON (((1743890.310 5905057.188, 17438...
33	Whau	MULTIPOLYGON (((1748168.244 5916597.058, 17481...

	OBJECTID	CONTOUR	created_da	DocumentUR	GlobalID	GROUPZONE	GROUPZONE_	last_edite	NAME	PARCEL_BAS	...	TYPE	TYPE_resol	VERSIONSTA	VERSIONS_1	ZONE	ZONE_resol	ZONEHEIGHT	SHAPE_Leng	SHAPE_Area	geometry
0	1.0	None	20160718211	None	{4C8F9436-7EA6-417E-B64F-15FCD44459F6}	2	Residential	20161111010	None	None	...	None	None	4	Operative	60	Residential - Mixed Housing Urban Zone	NaN	285.664016	2.050275e+03	POLYGON ((1768030.306 5901206.846, 1768033.070...
1	2.0	None	20160718211	None	{604AAD87-8ED4-4111-8276-47CEE7E81F92}	1	Public Open Space	20161111010	None	None	...	None	None	4	Operative	33	Open Space - Sport and Active Recreation Zone	NaN	1246.837757	1.684599e+04	POLYGON ((1764267.286 5919989.370, 1764218.153...
2	3.0	None	20160718211	None	{8D827DA8-BC5B-437A-B17A-532354F7D037}	4	Rural	20161111010	None	None	...	None	None	4	Operative	11	Rural - Mixed Rural Zone	NaN	3582.113246	6.841744e+05	POLYGON ((1740091.195 5928308.839, 1740089.844...
3	4.0	None	20160718211	None	{96C9E266-3341-4C71-94F1-325F2EE45732}	2	Residential	20161111010	None	None	...	None	None	4	Operative	23	Residential - Large Lot Zone	NaN	317.098469	6.024226e+03	POLYGON ((1750502.125 5928677.635, 1750456.131...
4	5.0	None	20160718211	None	{90B50FEE-45A3-4E88-819A-370751ACDE3D}	1	Public Open Space	20161111010	None	None	...	None	None	4	Operative	31	Open Space - Conservation Zone	NaN	230.836636	5.639794e+02	POLYGON ((1741460.217 5918191.600, 1741476.745...
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
130295	130296.0	None	20161115151	None	{B2F0FB45-80F6-41ED-AA57-A914B195B31E}	1	Public Open Space	20161115151	None	None	...	None	None	4	Operative	31	Open Space - Conservation Zone	NaN	179382.307787	9.040193e+07	POLYGON ((1738072.631 5902593.421, 1738095.386...
130296	130297.0	None	20161115151	None	{A3F4EF61-9162-43C3-90BD-DC84D93C6A64}	2	Residential	20161115151	None	None	...	None	None	4	Operative	18	Residential - Mixed Housing Suburban Zone	NaN	2769.292040	2.920602e+05	POLYGON ((1770189.642 5905789.775, 1770199.407...
130297	130298.0	None	20161115151	None	{A5FC7EB5-76E5-414C-96DD-715C671764B4}	4	Rural	20161115151	Kaipara South Head and Harbour coastal area	None	...	None	None	4	Operative	46	Rural - Rural Coastal Zone	NaN	27168.762393	6.344938e+06	POLYGON ((1730253.401 5955767.484, 1730254.399...
130298	130299.0	None	20161115151	None	{C371B83C-35CE-46A1-B94F-F1E592F271F7}	2	Residential	20161115151	None	None	...	None	None	4	Operative	8	Residential - Terrace Housing and Apartment Bu...	NaN	1812.109533	1.536829e+05	POLYGON ((1743032.759 5924130.564, 1742996.886...
130299	130300.0	None	20161115151	None	{31281924-C74D-4038-8260-FE6D886F4C94}	2	Residential	20161115151	None	None	...	None	None	4	Operative	18	Residential - Mixed Housing Suburban Zone	NaN	2511.267945	3.539606e+05	POLYGON ((1748979.767 5925771.895, 1748986.615...

	OBJECTID	CONTOUR	created_da	DocumentUR	GlobalID	GROUPZONE	GROUPZONE_	last_edite	NAME	PARCEL_BAS	...	TYPE	TYPE_resol	VERSIONSTA	VERSIONS_1	ZONE	ZONE_resol	ZONEHEIGHT	SHAPE_Leng	SHAPE_Area	geometry
20853	20854.0	None	20160718211	None	{2D83F680-9587-4D57-AF0B-CB7CA27C3D2C}	6	Special purpose zone	20161111011	None	None	...	None	None	4	Operative	58	None	NaN	72.629617	22.286945	POLYGON ((1767684.860 5903714.023, 1767691.955...
121765	121766.0	None	20160718211	None	{FD3E8BB4-1979-42B5-9A77-5D04AE5190AF}	6	Special purpose zone	20161111010	None	None	...	None	None	4	Operative	58	None	NaN	86.135341	4.114962	POLYGON ((1767684.083 5903713.690, 1767666.954...

	Area_Code_and_Description	Code	Description	2001_Census_total_households_in_occupied_private_dwellings	2006_Census_total_households_in_occupied_private_dwellings	2013_Census_total_households_in_occupied_private_dwellings	2001_Census_household_composition_for_households_in_occupied_private_dwellings_One-family_household_(with_or_without_other_people)	2001_Census_household_composition_for_households_in_occupied_private_dwellings_Two-family_household_(with_or_without_other_people)	2001_Census_household_composition_for_households_in_occupied_private_dwellings_Three_or_more_family_household_(with_or_without_other_people)	2001_Census_household_composition_for_households_in_occupied_private_dwellings_Other_multi-person_household	...	2006_Census_access_to_telecommunications(20)(21)_for_households_in_occupied_private_dwellings_Not_Elsewhere_Included(22)	2006_Census_access_to_telecommunications(20)(21)_for_households_in_occupied_private_dwellings_Total_households_in_occupied_private_dwellings	2013_Census_access_to_telecommunications(20)(21)_for_households_in_occupied_private_dwellings_No_Access_to_Telecommunication_Systems	2013_Census_access_to_telecommunications(20)(21)_for_households_in_occupied_private_dwellings_Access_to_a_Cellphone/Mobile_Phone	2013_Census_access_to_telecommunications(20)(21)_for_households_in_occupied_private_dwellings_Access_to_a_Telephone	2013_Census_access_to_telecommunications(20)(21)_for_households_in_occupied_private_dwellings_Access_to_a_Fax_Machine	2013_Census_access_to_telecommunications(20)(21)_for_households_in_occupied_private_dwellings_Access_to_the_Internet	2013_Census_access_to_telecommunications(20)(21)_for_households_in_occupied_private_dwellings_Total_households_stated	2013_Census_access_to_telecommunications(20)(21)_for_households_in_occupied_private_dwellings_Not_Elsewhere_Included(22)	2013_Census_access_to_telecommunications(20)(21)_for_households_in_occupied_private_dwellings_Total_households_in_occupied_private_dwellings
0	MB 0000100	100	NaN	3.0	3.0	3.0	..C	..C	..C	..C	...	..C	3.0	..C	..C	..C	..C	..C	..C	..C	3.0
1	MB 0000200	200	NaN	27.0	27.0	30.0	15	3	0	0	...	3	24.0	3	9	24	6	15	30	3	30.0
2	MB 0000300	300	NaN	21.0	24.0	21.0	18	3	0	0	...	0	24.0	0	6	12	3	9	18	3	18.0
3	MB 0000400	400	NaN	9.0	9.0	9.0	6	..C	..C	..C	...	..C	12.0	..C	..C	9	..C	6	9	..C	9.0
4	MB 0000501	501	NaN	0.0	0.0	0.0	..C	..C	..C	..C	...	..C	0.0	..C	..C	..C	..C	..C	..C	..C	0.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
49038	Footnotes for specific variables or categories	23	Median total household income is rounded to th...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
49039	Symbols	..C	Confidential	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
49040	Symbols	..	not available	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
49041	Symbols	*	not able to be calculated	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
49042	Source	Statistics New Zealand	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN

Phase 2: Geographic Datasets¶

Outputs¶

Local area dataset¶

Meshblock 2018 dataset:¶

	Name	Coast_indicator
OBJECTID
1	Auckland Central	True
2	Beach Haven-Birkenhead-Northcote	True
3	Botany	True
4	Devonport-Takapuna	True
5	East Coast Bays	True
6	Franklin-Beachlands-Hunua	True
7	Franklin-Pukekohe	True
8	Franklin-Waiuku	True
9	Henderson-Massey	True
10	Hibiscus Coast	True
11	Howick	True
12	Mangere-Otahuhu	True
13	Manurewa	True
14	Maungakiekie	True
15	Mt Albert	False
16	Mt Eden	True
17	Orakei	True
18	Otara	True
19	Pakuranga	True
20	Papakura	True
21	Papatoetoe	True
22	Puketapapa	True
23	Rodney-Dairy Flat	True
24	Rodney-Helensville	True
25	Rodney-Kumeu-Riverhead	True
26	Rodney-Warkworth	True
27	Rodney-Wellsford	True
28	Tamaki	True
29	Titirangi	True
30	Upper Harbour Local Board Area	True
31	Waiheke	True
32	Waitakere	True
33	Whau	True

	Meshblock	MeshblockNumber	Population_Count_Usual_Resident_2013	Population_Count_Census_Night_2013	geometry
0	MB 0352700	0352700	0	0	MULTIPOLYGON (((1753237.550 5923918.395, 17532...
1	MB 0728500	0728500	9	6	MULTIPOLYGON (((1761011.438 5905840.848, 17610...
2	MB 0829300	0829300	93	99	MULTIPOLYGON (((1739739.656 5899165.556, 17397...
3	MB 1280801	1280801	0	0	MULTIPOLYGON (((1869852.595 5695096.558, 18714...
4	MB 2360001	2360001	0	0	MULTIPOLYGON (((1623601.773 5423210.098, 16235...
...	...	...	...	...	...
46616	MB 0074002	0074002	0	0	MULTIPOLYGON (((1741178.600 6046572.236, 17414...
46617	MB 3208002	3208002	78	81	MULTIPOLYGON (((1770892.430 5911519.906, 17708...
46618	MB 3208003	3208003	36	36	MULTIPOLYGON (((1771025.156 5911674.629, 17709...
46619	MB 3209001	3209001	84	87	MULTIPOLYGON (((1771731.425 5912665.799, 17717...
46620	MB 3209002	3209002	75	81	MULTIPOLYGON (((1771758.103 5912422.760, 17717...

	Name	Census2013_population	Census2013_dwellings	Census2013_avg_HH_income
OBJECTID
9	Henderson-Massey	107658	34461.0	68347.842761
2	Beach Haven-Birkenhead-Northcote	82431	28344.0	80154.552207
13	Manurewa	82230	22584.0	69202.861326
17	Orakei	79581	29001.0	110143.425140
1	Auckland Central	77058	31506.0	86499.398108
33	Whau	72585	23928.0	65948.529781
12	Mangere-Otahuhu	70998	17427.0	61855.553636
4	Devonport-Takapuna	55398	20307.0	89560.517751
30	Upper Harbour Local Board Area	53571	17079.0	92354.882984
22	Puketapapa	52959	16695.0	75460.348858
15	Mt Albert	47913	15630.0	97670.686671
3	Botany	46878	13893.0	93325.464764
16	Mt Eden	46752	16155.0	83313.629602
20	Papakura	45687	14919.0	68043.070707
21	Papatoetoe	45633	13272.0	65055.631090
10	Hibiscus Coast	45141	17397.0	70786.765722
5	East Coast Bays	44634	15354.0	96602.171362
28	Tamaki	42189	13551.0	64390.277469
11	Howick	40299	13836.0	85281.267636
19	Pakuranga	39924	13206.0	82096.046353
29	Titirangi	35958	12192.0	80713.729357
7	Franklin-Pukekohe	31176	10863.0	78094.119279
18	Otara	30033	6693.0	58229.733394
14	Maungakiekie	27780	10368.0	76915.212333
6	Franklin-Beachlands-Hunua	21111	7332.0	97416.701031
24	Rodney-Helensville	17832	6381.0	78202.750119
26	Rodney-Warkworth	17637	6912.0	62686.367596
8	Franklin-Waiuku	13581	5046.0	68150.358852
32	Waitakere	12477	4449.0	84773.121192
31	Waiheke	8310	3597.0	55066.946309
25	Rodney-Kumeu-Riverhead	7257	2472.0	86943.048780
23	Rodney-Dairy Flat	6564	2130.0	106361.971831
27	Rodney-Wellsford	5595	2115.0	51240.028490

	Code	SA12018_V1_00	SA22018_V1_00	SA22018_V1_00_NAME	UR2018_V1_00	UR2018_V1_00_NAME	IUR2018_V1_00	IUR2018_V1_00_NAME	CB2018_V1_00	CB2018_V1_00_NAME	...	WARD2018_V1_00	WARD2018_V1_00_NAME	LANDWATER	LANDWATER_NAME	LAND_AREA_SQ_KM	AREA_SQ_KM	Shape_Length	geometry	Centroid_lon	Centroid_lat
543	0137200	7001157	110400	Cape Rodney	1098	Other rural Auckland	22	Rural other	07601	Rodney Local Board Area	...	07601	Rodney Ward	11	Island	0.097567	0.097567	1356.404986	MULTIPOLYGON (((1761489.925 5985285.678, 17614...	1.761464e+06	5.985098e+06
544	0170700	7001317	114300	Gulf Islands	1098	Other rural Auckland	22	Rural other	07602	Hibiscus and Bays Local Board Area	...	07602	Albany Ward	11	Island	0.005545	0.005545	321.185380	MULTIPOLYGON (((1753753.763 5954256.206, 17537...	1.753808e+06	5.954210e+06
545	0438700	7001130	111800	Barrier Islands	1098	Other rural Auckland	22	Rural other	07608	Great Barrier Local Board Area	...	07605	Waitemata and Gulf Ward	11	Island	70.725594	70.725594	100201.649851	MULTIPOLYGON (((1815954.125 6007877.906, 18159...	1.814416e+06	6.001384e+06
546	0439306	7001135	111800	Barrier Islands	1109	Tryphena	21	Rural settlement	07608	Great Barrier Local Board Area	...	07605	Waitemata and Gulf Ward	11	Island	0.078233	0.078233	1494.370224	MULTIPOLYGON (((1823569.424 5979782.392, 18235...	1.823537e+06	5.979605e+06
547	0439307	7001135	111800	Barrier Islands	1109	Tryphena	21	Rural settlement	07608	Great Barrier Local Board Area	...	07605	Waitemata and Gulf Ward	11	Island	0.475337	0.475337	3408.621475	MULTIPOLYGON (((1824128.365 5979223.530, 18242...	1.823950e+06	5.978887e+06
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
53299	4011884	7001155	110400	Cape Rodney	1073	Whangateau	21	Rural settlement	07601	Rodney Local Board Area	...	07601	Rodney Ward	12	Mainland	1.161280	1.161280	7266.530123	MULTIPOLYGON (((1759217.632 5980700.819, 17596...	1.758846e+06	5.980192e+06
53301	4011883	7001154	110400	Cape Rodney	1098	Other rural Auckland	22	Rural other	07601	Rodney Local Board Area	...	07601	Rodney Ward	12	Mainland	7.680889	7.680889	17076.458542	MULTIPOLYGON (((1756196.931 5982761.262, 17562...	1.757339e+06	5.980998e+06
53333	4011925	7009199	156900	Baverstock	1108	Auckland	11	Major urban area	07616	Howick Local Board Area	...	07610	Howick Ward	12	Mainland	0.242189	0.242189	2352.598074	MULTIPOLYGON (((1770681.901 5909197.803, 17711...	1.771009e+06	5.908956e+06
53359	4011971	7009313	158600	Ormiston East	1108	Auckland	11	Major urban area	07616	Howick Local Board Area	...	07610	Howick Ward	12	Mainland	0.487587	0.487587	3692.342983	MULTIPOLYGON (((1771223.196 5906518.069, 17715...	1.771828e+06	5.906150e+06
53360	4011972	7009335	158900	Tuscany Heights	1108	Auckland	11	Major urban area	07616	Howick Local Board Area	...	07610	Howick Ward	12	Mainland	0.178460	0.178460	1798.203501	MULTIPOLYGON (((1772642.183 5906246.394, 17726...	1.772697e+06	5.905962e+06

	geometry	legs	distance	duration	weight_name	weight
0	LINESTRING (174.76202 -36.84851, 174.76208 -36...	[{'steps': [], 'distance': 6623.4, 'duration':...	6623.4	471.2	routability	482.5
1	LINESTRING (174.76202 -36.84851, 174.76197 -36...	[{'steps': [], 'distance': 1487.9, 'duration':...	1487.9	201.1	routability	212.4
2	LINESTRING (174.76202 -36.84851, 174.76208 -36...	[{'steps': [], 'distance': 11247.2, 'duration'...	11247.2	783.1	routability	794.4
3	LINESTRING (174.76202 -36.84851, 174.76197 -36...	[{'steps': [], 'distance': 1022.2, 'duration':...	1022.2	135.0	routability	146.3

	Mdist_skytower_center	Mdist_skytower_west	Mdist_skytower_east
count	13441.000000	13441.000000	13441.000000
mean	21083.293989	21076.932974	21080.920289
std	15682.240482	15680.363394	15684.086849
min	59.500000	110.700000	155.200000
25%	11506.700000	11485.800000	11514.800000
50%	17201.300000	17202.300000	17154.200000
75%	24218.100000	24203.500000	24235.700000
max	136633.200000	136633.200000	136633.200000